__author__ = 'joey'

from math import *
import operator

# 1 entropy
dataSet = [[1, 1, 'yes'], [1, 1, 'yes'], [1, 0, 'no'], [0, 1, 'no'], [0, 1, 'no']]
labels1 = ['no surfacing', 'flippers']
#numEntries = len(dataSet)
#labelCounts = {}
#for featVec in dataSet:
#    currentLabel = featVec[-1]
#    labelCounts[currentLabel] = labelCounts.get(currentLabel, 0) + 1
#print labelCounts
#shannonEnt = 0.0
#for key in labelCounts:
#    prob = float(labelCounts[key]) / numEntries
#    shannonEnt -= prob * log(prob, 2)
#print shannonEnt

# split data set
#axis = 1
#value = 1
#retDataSet = []
#for featVec in dataSet:
#    if featVec[axis] == value:
#        reducedFeatVec = featVec[:axis]
#        reducedFeatVec.extend(featVec[axis + 1:])
#        retDataSet.append(reducedFeatVec)
#print retDataSet

# 3 choose best feature to split
def splitDataSet(dataSet, axis, value):
    retDataSet = []
    for featVec in dataSet:
        if featVec[axis] == value:
            reducedFeatVec = featVec[:axis]
            reducedFeatVec.extend(featVec[axis + 1:])
            retDataSet.append(reducedFeatVec)
    return retDataSet

def calcShannonEnt(dataSet):
    numEntries = len(dataSet)
    labelCounts = {}
    for featVec in dataSet:
        currentLabel = featVec[-1]
        labelCounts[currentLabel] = labelCounts.get(currentLabel, 0) + 1
    shannonEnt = 0.0
    for key in labelCounts:
        prob = float(labelCounts[key]) / numEntries
        shannonEnt -= prob * log(prob, 2)
    return shannonEnt

def chooseBestFeatureToSplit(dataSet):
    numFeatures = len(dataSet[0]) - 1
    baseEntropy = calcShannonEnt(dataSet)
    bestInfoGain = 0.0
    bestFeature = -1
    for i in range(numFeatures):
        featList = [example[i] for example in dataSet]
        uniqueVals = set(featList)
        newEntropy = 0.0
        for value in uniqueVals:
            subDataSet = splitDataSet(dataSet, i, value)
            prob = len(subDataSet) / float(len(dataSet))
            newEntropy += prob * calcShannonEnt(subDataSet)
        infoGain = baseEntropy - newEntropy
        if infoGain > bestInfoGain:
            bestInfoGain = infoGain
            bestFeature = i
    return bestFeature

# 4 majority cnt
def majorityCnt(classList):
    classCount = {}
    for vote in classList:
        classCount[vote] = classCount.get(vote, 0) + 1
    sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1),reverse=True)
    return sortedClassCount[0][0]

def createTree(dataSet, labels):
    classList = [example[-1] for example in dataSet]
    if classList.count(classList[0]) == len(classList):
        return classList[0]
    if len(dataSet[0]) == 1:
        return majorityCnt(classList)
    bestFeat = chooseBestFeatureToSplit(dataSet)
    bestFeatLabel = labels[bestFeat]
    myTree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)
    return myTree
myTree1 = createTree(dataSet, labels1)
print myTree1


# 5 create tree
classList = [example[-1] for example in dataSet]
print classList
print classList.count(classList[0])
print len(dataSet[0])

# 6 create tree plotter
# import matplotlib.pyplot as plt
#
# decisionNode = dict(boxstyle="sawtooth", fc="0.8")
# leafNode = dict(boxstyle="round4", fc="0.8")
# arrow_args = dict(arrowstyle="<-")
#
# def plotNode(nodeTxt, centerPt, parentPt, nodeType):
#     createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction', xytext=centerPt, textcoords='axes fraction', bbox=nodeType, arrowprops=arrow_args)
#
# def createPlot():
#     fig = plt.figure(1, facecolor='white')
#     fig.clf()
#     createPlot.ax1 = plt.subplot(111, frameon=False)
#     plotNode('d', (0.5, 0.1), (0.1, 0.5), decisionNode)
#     plotNode('l', (0.8, 0.1), (0.3, 0.8), leafNode)
#     plt.show()
#
# createPlot()

# 7 get num leafs
def getNumLeafs(myTree):
    numLeafs = 0
    firstStr = myTree.keys()[0]
    secondDict = myTree[firstStr]
    for key in secondDict.keys():
        if type(secondDict[key]).__name__ == 'dict':
            numLeafs += getNumLeafs(secondDict[key])
        else:
            numLeafs += 1
    return numLeafs

def getTreeDepth(myTree):
    maxDepth = 0
    firstStr = myTree.keys()[0]
    secondDict = myTree[firstStr]
    for key in secondDict.keys():
        if type(secondDict[key]).__name__ == 'dict':
            thisDepth = 1 + getTreeDepth(secondDict[key])
        else:
            thisDepth = 1
        if thisDepth > maxDepth:
            maxDepth = thisDepth
    return maxDepth

print "leafs:%s" % getNumLeafs(myTree1)
print "depth:%s" % getTreeDepth(myTree1)

import matplotlib.pyplot as plt

decisionNode = dict(boxstyle="sawtooth", fc="0.8")
leafNode = dict(boxstyle="round4", fc="0.8")
arrow_args = dict(arrowstyle="<-")

def plotNode(nodeTxt, centerPt, parentPt, nodeType):
    createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction', xytext=centerPt, textcoords='axes fraction', bbox=nodeType, arrowprops=arrow_args)

def plotMidText(cntrPt, parentPt, txtString):
    xMid = (parentPt[0]-cntrPt[0]) / 2.0 + cntrPt[0]
    yMid = (parentPt[1]-cntrPt[1]) / 2.0 + cntrPt[1]
    createPlot.ax1.text(xMid, yMid, txtString)

def plotTree(myTree, parentPt, nodeTxt):
    numLeafs = getNumLeafs(myTree)
    depth = getTreeDepth(myTree)
    firstStr = myTree.keys()[0]
    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs)) / 2.0 / plotTree.totalW, plotTree.yOff)
    plotMidText(cntrPt, parentPt, nodeTxt)
    plotNode(firstStr, cntrPt, parentPt, decisionNode)
    secondDict = myTree[firstStr]
    plotTree.yOff = plotTree.yOff - 1.0 / plotTree.totalD
    for key in secondDict.keys():
        if type(secondDict[key]).__name__ == 'dict':
            plotTree(secondDict[key], cntrPt, str(key))
        else:
            plotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalW
            plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)
            plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))
    plotTree.yOff = plotTree.yOff + 1.0 / plotTree.totalD

def createPlot(inTree):
    fig = plt.figure(1,facecolor='white')
    fig.clf()
    axprops = dict(xticks=[], yticks=[])
    createPlot.ax1 = plt.subplot(111,frameon=False, **axprops)
    plotTree.totalW = float(getNumLeafs(inTree))
    plotTree.totalD = float(getTreeDepth(inTree))
    plotTree.xOff = -0.5 / plotTree.totalW
    plotTree.yOff = 1.0
    plotTree(inTree, (0.5, 1.0), '')
    plt.show()

createPlot(myTree1)

def classify(inputTree, featLabels, testVec):
    firstStr = inputTree.keys()[0]
    secondDict = inputTree[firstStr]
    featIndex = featLabels.index(firstStr)
    for key in secondDict.keys():
        if testVec[featIndex] == key:
            if type(secondDict[key]).__name__ == 'dict':
                classLabel = classify(secondDict[key], featLabels, testVec)
            else:
                classLabel = secondDict[key]
    return classLabel

labels1 = ['no surfacing', 'flippers']
print classify(myTree1, labels1, [1, 0])

def storeTree(inputTree, filename):
    import pickle
    fw = open(filename, 'w')
    pickle.dump(inputTree, fw)
    fw.close()

def grabTree(filename):
    import pickle
    fr = open(filename)
    return pickle.load(fr)

storeTree(myTree1, 'trees.txt')
print grabTree('trees.txt')


